Commenced in January 2007
Paper Count: 31023
A Fast Object Detection Method with Rotation Invariant Features
Abstract:Based on the combined shape feature and texture feature, a fast object detection method with rotation invariant features is proposed in this paper. A quick template matching scheme based online learning designed for online applications is also introduced in this paper. The experimental results have shown that the proposed approach has the features of lower computation complexity and higher detection rate, while keeping almost the same performance compared to the HOG-based method, and can be more suitable for run time applications.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082049Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2204
 N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. In CVPR, 2005.
 M.Grabner, H.Grabner, and H.Bischof. Semi-supervised on-line boosting for robust tracking. In ECCV, 2008.
 Timo Ojala, Matti and Topi. Multiresolution Gray-Scale and Rotation Invariiant Texture Classification with Local Binary Patterns. IEEE Trans on Pattern Analysis And Machine Intelligence, 2002.
 Qing Jun Wang and Ru Bo Zhang. LPP-HOG: A New Local Image Descriptor for Fast Human Detection. In KAM, 2008.
 Hui-Xing Jia, Yu-Jin Zhang. Fast Human Detection by Boosting Histograms of Oriented Gradients. In ICIG, 2007.
 Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Pascal Fua and Nassir Navab. Dominant Orientation Templates for Real-Time Detection of Texture-Less Objects. In CVPR, 2009.
 Simon Taylor and Tom Drummond. Multiple Target Localisation at over 100 FPS. In BMVC, 2009.
 M.Ozuysal, P.Fua, and V.Lepetit. Fast key point recognition in ten lines of code. In CVPR, 2007.
 P. Viola and M. Jones. Robust real-time object detection. IJCV, 2001.